{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#竞赛的评价指标为logloss\n",
    "from sklearn.metrics import log_loss  \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = \"C:/Users/18811/Downloads/Compressed/pima Indians Diabetes Data/\"\n",
    "train = pd.read_csv(dpath+\"diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose 5\n",
      "Glucose 0.006510416666666667\n",
      "BloodPressure 35\n",
      "BloodPressure 0.045572916666666664\n",
      "SkinThickness 227\n",
      "SkinThickness 0.2955729166666667\n",
      "Insulin 374\n",
      "Insulin 0.4869791666666667\n",
      "BMI 11\n",
      "BMI 0.014322916666666666\n"
     ]
    }
   ],
   "source": [
    "#由上可知，从Glucose开始到BMI均有缺失值,判断各列缺失值个数及比例\n",
    "f_zero = train.columns[1:6]\n",
    "for i in f_zero:\n",
    "    j = train[i]\n",
    "    zero = j.values==0\n",
    "    a = j[zero] \n",
    "    zero_count=len(a)\n",
    "    print(i,zero_count)\n",
    "    print(i,zero_count/train[i].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#缺失值存在时各类别分布\n",
    "columns=train.columns[1:6]\n",
    "fig=plt.figure(figsize=(10,10))\n",
    "for i,column in enumerate(columns):\n",
    "    ax=fig.add_subplot(3,2,i+1)\n",
    "    sns.violinplot(train[\"Outcome\"],train[column])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#缺失值不存在时各类别分布，差异不大，因此缺失值大的直接丢掉，小的用各类别的平均值填充\n",
    "fig=plt.figure(figsize=(10,10))\n",
    "for i,column in enumerate(columns):\n",
    "    ax=fig.add_subplot(3,2,i+1)\n",
    "    new=pd.concat([train[\"Outcome\"],train[column]],axis=1)\n",
    "    a=new.drop(new.loc[new[column]==0].index)\n",
    "    sns.violinplot(a[\"Outcome\"],a[column]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除缺失值大的特征\n",
    "train=train.drop([\"SkinThickness\",\"Insulin\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121.68676277850591\n",
      "72.40518417462482\n",
      "32.457463672391015\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.845052\n",
       "Glucose                     121.686763\n",
       "BloodPressure                72.405184\n",
       "BMI                          32.457464\n",
       "DiabetesPedigreeFunction      0.471876\n",
       "Age                          33.240885\n",
       "Outcome                       0.348958\n",
       "dtype: float64"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#缺失值处理\n",
    "columns=train.columns[1:4]\n",
    "# train[columns]=train[columns].replace({0:\"NaN\"})\n",
    "for column in columns:\n",
    "    a=train[column].values.tolist()\n",
    "    for i in a:\n",
    "        if i==0:\n",
    "            a.remove(i)\n",
    "    rep=train[column].values.sum()/len(a)\n",
    "    print(rep)\n",
    "    train[column]=train[column].replace({0:rep})\n",
    "train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Target 分布，看看各类样本分布是否均衡\n",
    "sns.countplot(train.Outcome);\n",
    "pyplot.xlabel('Outcome');\n",
    "pyplot.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24143e267f0>"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看各数值型特征间的相关性\n",
    "corr = train.corr().abs()\n",
    "pyplot.subplots(figsize=(10, 9))\n",
    "sns.heatmap(corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train[\"Outcome\"].values\n",
    "X_train= train.drop(\"Outcome\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.63994726,  0.84832379,  0.14964075,  0.20401277,  0.46849198,\n",
       "         1.4259954 ],\n",
       "       [-0.84488505, -1.12339636, -0.16054575, -0.68442195, -0.36506078,\n",
       "        -0.19067191],\n",
       "       [ 1.23388019,  1.94372388, -0.26394125, -1.10325546,  0.60439732,\n",
       "        -0.10558415],\n",
       "       ...,\n",
       "       [ 0.3429808 ,  0.00330087,  0.14964075, -0.73518964, -0.68519336,\n",
       "        -0.27575966],\n",
       "       [-0.84488505,  0.1597866 , -0.47073225, -0.24020459, -0.37110101,\n",
       "         1.17073215],\n",
       "       [-0.84488505, -0.8730192 ,  0.04624525, -0.20212881, -0.47378505,\n",
       "        -0.87137393]])"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "#默认logistic regression\n",
    "#初始化模型\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.49225723 0.51050369 0.48045374 0.43394561 0.48922605]\n",
      "cv logloss is: 0.4812772614858808\n"
     ]
    }
   ],
   "source": [
    "#分类任务中使用交叉验证StratifiedKFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print('logloss of each fold is: ',-loss)\n",
    "print('cv logloss is:', -loss.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l1: 0.48144246422530257\n",
      "l1: {'C': 0.8999999999999996, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "#l1正则\n",
    "l1_penaltys = ['l1']\n",
    "l1_Cs = np.arange(0.1,1,0.01)\n",
    "#l1_Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "l1_tuned_parameters = dict(penalty = l1_penaltys, C = l1_Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, l1_tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)\n",
    "# examine the best model\n",
    "print(\"l1:\",-grid.best_score_)\n",
    "print(\"l1:\",grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l2: 0.48110134868492666\n",
      "l2: {'C': 0.2899999999999999, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "#l2正则\n",
    "l2_penaltys = ['l2']\n",
    "l2_Cs = np.arange(0.1,0.5,0.01)\n",
    "#l2_Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "l2_tuned_parameters = dict(penalty = l2_penaltys, C = l2_Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, l2_tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)\n",
    "# examine the best model\n",
    "print(\"l2:\",-grid.best_score_)\n",
    "print(\"l2:\",grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l2: 0.7682291666666666\n",
      "l2: {'C': 0.01, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "#LinearSVC l2正则\n",
    "from sklearn.svm import LinearSVC\n",
    "l2_penaltys = ['l2']\n",
    "l2_Cs = np.arange(0.005,0.02,0.001)\n",
    "#l2_Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "l2_tuned_parameters = dict(penalty = l2_penaltys, C = l2_Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, l2_tuned_parameters,cv=5)\n",
    "grid.fit(X_train,y_train)\n",
    "# examine the best model\n",
    "print(\"l2:\",grid.best_score_)\n",
    "print(\"l2:\",grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l2: 0.7786458333333334\n",
      "l2: {'C': 100, 'gamma': 0.01291549665014884, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "#RBFSVC l2正则\n",
    "from sklearn.svm import SVC\n",
    "gamma_s = np.logspace(-3, -1, 10) \n",
    "#l2_Cs = np.arange(0.005,0.02,0.001)\n",
    "l2_Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "l2_tuned_parameters = dict(kernel=[\"rbf\"],C = l2_Cs,gamma=gamma_s)\n",
    "\n",
    "lr_penalty= SVC()\n",
    "grid= GridSearchCV(lr_penalty, l2_tuned_parameters,cv=5)\n",
    "grid.fit(X_train,y_train)\n",
    "# examine the best model\n",
    "print(\"l2:\",grid.best_score_)\n",
    "print(\"l2:\",grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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